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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

引用

2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010

作者： Ramezani, Mahdi Heidari, Amin Fatemizadeh, Emad Soltanian-Zadeh, Hamid Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

ISBN: (纸本)9781424466238

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Brain

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18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Daras, Petros Semertzidis, Theodoros Makris, Lambros Strintzis, Michael G. Information Processing Laboratory Department of Electrical and Computer Engineering Aristotle University of Thessaloniki GR54124 Thessaloniki Greece Informatics and Telematics Institute Centre for Research and Technology Hellas GR57001 Thessaloniki Greece

ISBN: (纸本)9781605589336

Content searching and downloading are the two dominant actions of the Internet users today, despite the fact that the Internet was not originally architected to serve such actions. Content Centric Networking is the new trend in the research community to build network architectures that route content by name and not by the host's network address so as to efficiently deal with content persistence, availability and authenticity issues. In this work we propose an extension to the Content Centric Network protocol in order to support content search as a native process of the network. By using object descriptors to represent the actual content objects and integrate these descriptors in the network protocol we manage to search and retrieve content from the network not only by name but also by the content itself. In this approach, searching for information is a process distributed to the reachable network. Moreover, content aggregation is handled by the end user and not by content aggregation portals, thematic content search engines or information curators. By doing so, search is not any more an application but part of the network and can pave the way for many novel applications which have never been thought until today. © 2010 ACM.

关键词： Network architecture

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3D reconstruction of indoor and outdoor building scenes from a single image

3D reconstruction of indoor and outdoor building scenes from...

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2010 ACM Workshop on Surreal Media and Virtual Cloning, SMVC'10, Co-located with ACM Multimedia 2010

作者： Vouzounaras, Georgios Agapito, Juan Diego Perez-Moneo Daras, Petros Strintzis, Michael G. Department of Electrical and Computer Engineering Information Processing Laboratory Aristotle University of Thessaloniki GR54124 Thessaloniki Greece Centre for Research and Technology Informatics and Telematics Institute Hellas GR57001 Thessaloniki Greece

ISBN: (纸本)9781450301756

In this paper, a novel method is proposed able to automatically generate accurate 3D models of both outdoor buildings and indoor scenes with perspective cues from line segments that are automatically extracted from a single image with an uncalibrated camera. The proposed method uses geometric constraints and knowledge of photography and achieves an accurate, real-time and fully automated 3D reconstruction of the scene without any intervention from the user.

关键词： Three dimensional computer graphics

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A fast image registration algorithm for Digital Subtraction Angiography

A fast image registration algorithm for Digital Subtraction ...

引用

Iranian Conference of Biomedical engineering (ICBME)

作者： Mansour Nejati Rassoul Amirfattahi Saeed Sadri Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Digital Subtraction Angiography (DSA) is a powerful technique for the visualization of blood vessels. The major problem encountered in DSA images is the presence of motion artifacts which arise from the misalignment of successive images in the sequence. In this paper, a fast registration algorithm for coronary angiographic images is proposed. It involves a feature-based selection of control points for which the displacement is computed by means of template matching, and the complete displacement vector field is constructed by means of interpolation. To reduce the computational cost of the template matching procedure, an iterative hill-climbing approach is used for optimization and for handling the local misalignments, the image warping is performed based on multilevel B-spline interpolation.

关键词： Image edge detection Artificial neural networks Interpolation Pixel

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Automatic segmentation of pallet images using the 2-D wavelet transform and YUV color space

Automatic segmentation of pallet images using the 2-D wavele...

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Iranian Conference on electrical engineering (ICEE)

作者： Aida Fooladivanda Narges Chehrerazi Saeed Sadri Rasoul Amirfattahi Mohammad Ali Montazeri Digital signal processing research Laboratory Department of Electrical and computer engineering Isfahan University of Technology Isfahan Iran

ISBN: (纸本)9781424467600

Segmentation is an important stage in automatic digital image processing. A special case of segmentation is to segment objects from their background. Among different segmentation algorithm for object detection, learning based approach is widely applied. In steel industry, pallets are moving on a rail. They have high resolution details in their structure and the image of a pallet taken by a camera in real time suffers from severe noise and illumination variations. The purpose of this paper is to segment the pallet from a frame of a sequence of video images, such that the pallet is segmented without degradation of resolution. We use the pallet image in YUV color space together with wavelet transform (WT) for detection. For classification Support Vector Machine (SVM) is incorporated to the images. It is shown that the above procedure segments the pallets successfully without degradation of resolution.

关键词： Image segmentation Wavelet transforms Image resolution Degradation Support vector machines Support vector machine classification Digital images Object detection Metals industry Rails

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Online signature verification using combination of two classifiers

Online signature verification using combination of two class...

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Iranian Machine Vision and Image processing (MVIP)

作者： M. Saeidi R. Amirfattahi A. Amini M. Sajadi Digital Signal processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

The objective of signature verification is to distinguish forgery signature from genuine one. Online signature is the one which is registered through electronic devices such as digitizers and stored on computers in time sequence form. In this kind of signatures in addition to location information, time information such as speed and acceleration is stored. In this paper after accomplishment of some pre-processing procedures like normalization of signature size, smoothing and elimination of rotation on signatures using algorithms based on extremum matching of signals and ant colony, their time duration will be equalized. Afterwards, similarities between signatures will be determined using extended regression and finally will try to distinguish between forgery signatures from genuine one using support vector machine (SVM). The suggested online verification system is tested on SVC2004 signature set which is related to the first international signature verification competition and results are compared to respective results of participants. The results state that suggested method exhibits equal error rate (EER) of %4.3 in skilled forger group.

关键词： Forgery Mathematical model Equations Support vector machines Training Error analysis

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Techniques for automatic magnetic resonance image classification

Techniques for automatic magnetic resonance image classifica...

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International Conference on Genetic and Evolutionary Computing

作者： Chen, Hsian-Min Chen, Shih-Yu Chai, Jyh Wen Chen, Clayton Chi-Chang Wu, Chao-Cheng Ouyang, Yen-Chieh Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions. © 2010 IEEE.

关键词： Support vector machines

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A NEW APPROACH FOR ROBUST REALTIME VOICE ACTIVITY DETECTION USING SPECTRAL PATTERN

A NEW APPROACH FOR ROBUST REALTIME VOICE ACTIVITY DETECTION ...

引用

IEEE International Conference on Acoustics, Speech, and Signal processing

作者： M. H. Moattar M. M. Homayounpour Nima Khademi Kalantari Laboratory for Intelligent Signal and speech Processing Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran Iran Electrical Engineering Department Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9781424442959

In this paper a Voice Activity Detection approach is proposed which applies a voting algorithm to decide on the existence of speech in audio signal. For this purpose, the proposed approach uses three different short time features along with the pattern of spectral peaks of every frame. Spectral peaks pattern is appropriate for determining vowel sounds in speech signal even in the presence of noise. Therefore this measure can be applicable in voice activity detection in which the vowels characterize the speech signal. Experiments show that incorporating this measure along with our recently proposed approach for VAD, will improve the results of the algorithm considerably while imposing little computational overhead. The proposed approach is evaluated on different datasets with various noises and SNR levels and satisfying results are achieved.

关键词： Voice Activity Detection Spectral Peaks Pattern Spectral Flatness Activity Detection Voice Signal Voice Noise audio signals voice activity

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Volume-based magnetic resonance brain image classification

Volume-based magnetic resonance brain image classification

引用

International Conference on Genetic and Evolutionary Computing

作者： Chiou, Yaw-Jiunn Chai, Jyh-Wen Chen, Clayton Chi-Chang Chen, Shih-Yu Chen, Hsian-Min Ouyang, Yen-Chieh Su, Wu-Chung Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices-formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices. © 2010 IEEE.

关键词： Support vector machines

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

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International Conference on Intelligent and Advanced Systems, ICIAS

作者： Mahdi Ramezani Amin Heidari Emad Fatemizadeh Hamid Soltanian-Zadeh Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Clustering algorithms Algorithm design and analysis Correlation Approximation methods Independent component analysis Time series analysis Approximation algorithms

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